We would like to thank all of our users who have reported problems and made suggestions for improving this release. In particular, we thank Étienne Ayotte-Sauvé, Stephen Frank, Youngdae Kim, Andres Ramos, Steffen Rebennack, and Francisco Trespalacios.

new log output option lo=4 has been added: writes simultaneously to log file and stdout.

Memory leak for GUSS/ScenarioSolver fixed. This also was a problem for the GAMSModelInstance in the OO-API.

Command line option OptFile does not overwrite model.optfile anymore. OptFile now behaves as other GAMS options e.g. like ResLim. A new command line option ForceOptFile overwrites all other methods of setting a solver option.

New option NoNewVarEqu will trigger a compilation error when new equation or variable symbols are introduced. This is useful for testing GAMS run-time environments.

New option SymPrefix that prefixes all user symbols compiled in this run with the string value of this option before saving to a save/restart file. This is useful when merging multiple models together to avoid name clashes.

The "D" solvers GAMS/ConvertD and GAMS/CplexD started as research and development versions of the production solvers GAMS/Convert and GAMS/Cplex and offer some interesting new features that eventually will migrate into the production version. If you are unsure which version to use check the GAMS Support Wiki

The new solver ANTIGONE (Algorithms for coNTinuous / Integer Global Optimization of Nonlinear Equations) is a computational framework for deterministic global optimization of nonconvex MINLP. ANTIGONE performs equivalently to GloMIQO when all nonlinearities in MINLP are quadratic.

ANTIGONE has been developed by Computer-Aided Systems Laboratory at Princeton University; it was completed in collaboration with the Centre for Process Systems Engineering at Imperial College.

GAMS/ANTIGONE is available for the 32-bit and 64-bit versions of Windows and Linux.

GAMS/ANTIGONE requires the presence of a GAMS/CPLEX license and either a GAMS/CONOPT or a GAMS/SNOPT license.

An implementation of zero-half cuts is now available. So far, they may help only on a small subset of problems and may need some tuning, so they are off by default. The new option zerohalfcuts can be used to enable them.

Alternative implementations of Gomory mixed-integer and reduce and split cuts are now available. By default, these cuts are off. The new options gomorycuts2 and reduceandsplitcuts2 can be used to enable them.

Added new parameter cut_passes_slow to encourage the use of some of the more exotic/expensive cut generators.

The random seeds for CLP and CBC can now be set by user via the new options randomseedclp and randomseedcbc.

Cbc can now solve the root node multiple times, each with its own different seed. This can be enabled via the new option multiplerootpasses.

New option extravariables that switches on a trivial re-formulation that introduces extra integer variables to group together variables with same cost.

New option cutoffconstraint to add the objective function as a constraint which right hand side is set to the current cutoff value to the problem.

New option parallelmode to switch between deterministic and opportunistic parallelization. NOTE: The default is deterministic, while it was opportunistic in the past.

ConcurrentMIP: This new feature launches multiple, independent solves on the same MIP model, using different settings for each. The solve returns when the first one finishes. This approach allows you to exploit multiple cores to explore a diverse set of search strategies

NumericFocus: This new parameter allows you to indicate that a model is likely to experience numerical trouble, which then causes our internal algorithms to favor numerical robustness over speed

The new GAMS/Lindo/LindoGlobal option num_threads defines the number of threads to be used, it is initialized by the GAMS option threads

The new GAMS/Lindo/LindoGlobal option multithread_mode defines the threading mode (auto, concurrent or parallel)

Stochastic Solver Improvements

Nested Benders Decomposition implementation has been improved significantly, achieving speed factors up to 6X compared to the previous version

The Chance-programming solver adds a Genetic Algorithm to find high-quality feasible solutions to large-scale instances. Models in this class can now also be solved using the Simple Benders Decomposition method

Multithreading with Nested Benders Decomposition (NBD) solver leads to speed improvements from 2.5 to 3.5 when using 4 threads

MIP Solver Improvements

The heuristics are improved significantly, simple rounding and feasibility pump now use bound propagation to improve the current path to a new feasible MIP solution

Multithreading can lead to speed improvements from 1.5 to 3.0 times on difficult problems using 4 threads rather than 1, for easy MIP problems, e.g., < 600 seconds, multi-threading may give not much speedup

Multistart Solver Improvements

Multistart solver has been improved significantly, achieving speed factors up to 2X compared to the previous version

The likelihood of getting the global optimum has improved by 10-15% over a wide range of nonconvex models

Multithreading often leads to speed improvements from 2.0 to 3.0 times when using 4 threads, speed improvements tend to improve as the model size and the number of multistarts increase

Changed meaning of objwgt in chance constraints (CC): now it gets multiplied by the violation ratio of the CC before it was added to the objective, in previous versons it was multiplied by (1 - the violation ratio)

The new SULUM libraries comes with support for mixed-integer linear programs (MIP). The new MIP optimizer is an advanced implementation of a branch and cut method, with many performance enhancements added. The key features of the MIP optimizer can be highlighted as :

Advanced MIP presolve to reduce the problem size and provide a better formulation for the optimizer.

Tight integration with the Sulum LP optimizer to efficiently solve LP's in node and during heuristics.

Various branching and node selection methods from computational inexpensive to more expensive schemes.

Cutting plane generation and filtering if deemed necessary.

Heuristics to either find an initial solution or improve the current incumbent, which includes rounding, diving and sub-mipping heuristics types.

New features were added to the object oriented GAMS APIs including e.g. the capability to specify the domains of symbols, check for domain violations, copying ModelInstances, or setting the debug level using a environment variable. More details about new and modified functions can be found in the following sections about the different languages.

24.1.2 Maintenance release (June 16, 2013)

Table of Contents

We would like to thank all of our users who have reported problems and made suggestions for improving this release. In particular,
we thank Sergio Corvalan, George Mavrotas, Renger van Nieuwkoop, and Andres Ramos.

24.1.3 Maintenance release (July 26, 2013)

Table of Contents

We would like to thank all of our users who have reported problems and made suggestions for improving this release. In particular, we thank Wolfgang Britz, Sebastian Dilly, Sascha Herrmann, Aida Khajavirad, and Johan Villaume.